[TOC]
. vector_ Cache (hidden file): Glove vector storage address
captioning: The address where the text generated by the video description is stored
checkpoints: The address of the saved model file
data: Source video data
loader: Load data data
logs: Logs saved during model training, which can be used in conjunction with Tensorboardx
models: Model files
pycocoevalcap: Code for calculating indicators such as BLEU, CIDER, etc.
result: The address where the results of the trained model are stored
splits: The method of data cutting
config.py: Configuration file
run. py: Test model
train.py: training model+testing model
utils. py: Some public methods
- First, create a conda virtual environment through cyd.yaml (which stores the project's environment configuration)
Conda env create - f cyd.yaml
- After configuring the environment: directly run the train.py file (if the GPU graphics memory is insufficient, you can reduce the configuration through config)
- Generally no problem, ask me if you have any questions
Download link: https://pan.quark.cn/s/44049885ed0b
.vector_cache（隐藏文件）：glove 向量存放地址
captioning：视频描述生成的文本存放的地址
checkpoints：保存的模型文件的地址
data：源视频数据
loader：加载data数据
logs：训练模型时保存的日志，可配合 tensorboardx 一起使用
models：模型文件
pycocoevalcap：BLEU、CIDER…等指标计算的代码
result：训练好的模型的结果存放的地址
splits：数据切割的方式
config.py：配置文件
run.py：测试模型
train.py：训练模型+测试模型
utils.py：一些公共方法
- 首先通过 cyd.yaml（里面存放了项目的环境配置） 创建一个 conda 虚拟环境
conda env create -f cyd.yaml
- 配置好环境后：直接运行 train.py 文件（如果GPU 显存不够，可以通过 config 减小配置）
- 一般没问题，有问题问我
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